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Papers
61,005 resultsShowing papers similar to Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data
ClearSpatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models
Researchers used Landsat 8 satellite imagery and machine learning to assess the spatial extent and recovery trajectory of oil spill damage to coastal vegetation and wetlands in Nigeria, demonstrating that remote sensing combined with AI models can track long-term ecosystem recovery.
Diversity and distribution of seagrasses in Chilika Lagoon: Regional threats and management recommendations
Researchers surveyed seagrass diversity and spatial distribution across 57 sites in the Chilika Lagoon — Asia's largest brackish water lagoon — recording six species including Halophila ovalis, Halodule pinifolia, and Ruppia maritima, with the southern sector showing the highest species richness and density. The study identified key regional threats to seagrass meadows and provided management recommendations for this ecologically significant coastal lagoon system.
Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model
Researchers developed a deep neural network model applied to Landsat-8 satellite data to automatically identify water quality grades for lakes in the middle Yangtze River reaches, demonstrating that machine learning and remote sensing can provide cost-effective large-scale monitoring as an alternative to labor-intensive in situ measurements.
Enhanced spatiotemporal mapping of urban wetland microplastics: An interpretable CNN-GRU approach using satellite imagery and limited samples
Researchers built an interpretable CNN-GRU deep learning model combining satellite remote sensing with limited in-situ measurements to map microplastic distribution in urban wetlands with enhanced spatiotemporal resolution, enabling more comprehensive monitoring with less field sampling.
Evaluation of microplastic pollution in urban lentic ecosystem using remote sensing, GIS, and Support Vector Machine (SVM): relevance for environmental and ecological risk
Researchers assessed microplastic pollution in 24 urban ponds and lakes in Kolkata, India, finding significantly higher concentrations during the post-monsoon season, with fibers making up about 59% of all particles. They developed machine learning and remote sensing models that achieved up to 98% accuracy in identifying water bodies and predicting microplastic levels from satellite imagery. The study demonstrates that combining field sampling with remote sensing technology can enable large-scale monitoring of urban microplastic pollution.
Assessment of surface water dynamics through satellite mapping with Google Earth Engine and Sentinel-2 data in Manipur, India
Researchers used Google Earth Engine and Sentinel-2 satellite imagery to map seasonal surface water dynamics in Manipur, India, accurately tracking the extent and timing of water body changes across the region to support watershed planning.
Monitoring migratory birds of India's largest shallow saline Ramsar site (Sambhar Lake) using geospatial data for wetland restoration
Researchers monitored migratory bird populations at Sambhar Lake, India's largest shallow saline Ramsar site, using geospatial data and remote sensing tools to inform wetland restoration strategies. The study documented species composition and temporal abundance patterns to establish baseline data for conservation management of this critical stopover habitat.
SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements
Researchers developed SNOWED, an automatically constructed dataset of satellite imagery with labeled water edges, enabling deep learning models to accurately detect and monitor shoreline changes for environmental monitoring applications.
Coastal Marine Debris Detection and Density Mapping With Very High Resolution Satellite Imagery
Researchers used high-resolution satellite imagery combined with machine learning to detect and map coastal marine debris density in southern Japan, finding that satellite-based methods can estimate debris amounts and types on beaches with reasonable accuracy.
Machine learning in marine ecology: an overview of techniques and applications
This overview examines how machine learning techniques are being applied across marine ecology, covering data types from satellite imagery and acoustics to underwater images and genomic data. Researchers built a database of roughly 1,000 publications to map which techniques work best for different marine research questions. The study highlights that growing data volumes and computing power are making machine learning an increasingly essential tool for understanding ocean ecosystems.
Water Quality Monitoring And Ground Water Level Prediction Using Machine Learning
Researchers applied machine learning techniques to water quality monitoring and groundwater level prediction, demonstrating the potential of data-driven approaches for environmental sensing and resource management.
Distribution and Structure of China–ASEAN’s Intertidal Ecosystems: Insights from High-Precision, Satellite-Based Mapping
Researchers used multi-source satellite data to create high-precision maps of intertidal ecosystems across the China-ASEAN region, distinguishing between mangroves, salt marshes, and tidal flats. They developed an improved classification framework to address inconsistencies in previous mapping efforts. The study provides a valuable baseline for monitoring how climate change and human activities are affecting these ecologically important coastal zones.
Flux to Flow: a Clearer View of Earth’s Water Cycle Via Neural Networks and Satellite Data
This dissertation developed neural network methods to enhance the spatial resolution of satellite measurements of Earth's water cycle, enabling finer-scale monitoring of hydrological processes such as precipitation, evaporation, and runoff across diverse environments.
Regional Satellite Algorithms to Estimate Chlorophyll-a and Total Suspended Matter Concentrations in Vembanad Lake
Researchers developed regional satellite algorithms to estimate chlorophyll-a concentrations and total suspended matter in Vembanad Lake, India, using remote sensing data to monitor water quality in a highly productive but increasingly polluted coastal ecosystem. The algorithms were calibrated against in-situ measurements and found to improve the accuracy of water quality assessments compared to global ocean-color models, supporting sustainable development monitoring goals.
Predicting microplastic accumulation zones and shoreline changes along the Kelantan coast, Malaysia, using integrated GIS and ANN models
Researchers combined GIS with an artificial neural network to predict microplastic accumulation zones along Malaysia's Kelantan coast, achieving R=0.972 predictive accuracy and identifying shoreline erosion-prone areas as the primary deposition hotspots for microplastic pollution.
An Effective Machine Learning Scheme to Analyze and Predict the Concentration of Persistent Pollutants in the Great Lakes
Scientists applied multiple machine learning methods to predict concentrations of persistent organic pollutants in the Great Lakes, finding that LSTM neural networks outperformed simpler models for these complex time-series patterns. Similar predictive modeling could track microplastic concentrations in large water bodies over time.
Analysis of Land Use Evolution of Suzhou Wetlands Based on RS and GIS
Researchers used satellite remote sensing and GIS to track changes in land use and wetland coverage in Suzhou, China over time. Understanding how wetland ecosystems change is important for assessing their capacity to filter pollutants, including microplastics carried by stormwater and runoff.
Deep Learning Based Approach to Classify Saline Particles in Sea Water
Researchers developed a deep learning classification approach to identify saline particles in seawater images, demonstrating high accuracy in distinguishing salt crystals from other particles, with potential application to automated water quality monitoring systems.
Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake
Researchers used a combination of field measurements, weather data, and satellite imagery to estimate chlorophyll-a concentrations at different depths in a Chilean lake. They compared deep learning and statistical models and found all three approaches performed well for predicting algal levels in the freshwater ecosystem. The study advances water quality monitoring techniques that can help track environmental changes, including those potentially linked to pollution.
Modeling of daily groundwater level using deep learning neural networks
Researchers applied a CNN-biLSTM deep learning model to predict daily groundwater levels, finding it outperformed conventional modeling approaches by capturing both spatial patterns and temporal dependencies in the data. The method offers improved accuracy for groundwater monitoring, which is critical for managing increasingly stressed freshwater resources.
Long-Term and Bimonthly Estimation of Lake Water Extent Using Google Earth Engine and Landsat Data
Long-term bimonthly satellite estimates of lake water surface area were generated for numerous lakes using Google Earth Engine and Landsat imagery from the 1970s to the present. The method produced a reliable time series of lake area dynamics at high spatial resolution. Monitoring lake area changes is important for understanding how water availability is shifting under climate change.
Detection of Waste Plastics in the Environment: Application of Copernicus Earth Observation Data
Researchers developed a machine learning classifier using free Copernicus satellite data to detect plastic waste — including greenhouses, tyres, and waste sites — in both aquatic and terrestrial environments, achieving high accuracy and enabling low-cost large-scale plastic pollution mapping.
The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed
This paper is not about microplastics; it uses satellite rainfall data, HEC-RAS flood modeling, and artificial neural networks to predict flood inundation heights in the Majalaya Watershed of Indonesia.
Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis
Researchers used artificial neural network analysis to assess water quality and identify pollution causes in the Tuojiang River Basin in China, examining parameters including dissolved oxygen and ammonia-nitrogen to understand contamination patterns and risks in this waterway.